Guo Zhenghao, McClelland Verity M, Dai Wei, Cong Fengyu, Cvetkovic Zoran
Annu Int Conf IEEE Eng Med Biol Soc. 2024 Jul;2024:1-4. doi: 10.1109/EMBC53108.2024.10782732.
Functional cortico-muscular couplings are commonly assessed through cortico-muscular coherence (CMC) analysis, a measure of linear dependency between electroencephalogram (EEG) and electromyogram (EMG) signals. However, the presence of noise in EEG and EMG signals may exceed the strength of synchronous components, posing challenges in reliably detecting CMC. This study introduces an approach based on weighted errors-in-variables (EIV) modelling to extract relevant versions of cortical and muscular signals governing movement control from noisy EEG and EMG signals, aiming to enhance co-herence estimation. Two algorithms are presented for identifying the underlying EIV system: one employing total least squares and the other utilizing weighted total least squares, where knowledge of the unequal variance of observations is incorporated into the regression. The effectiveness of the proposed method is evaluated using synthetic and neurophysiological data, revealing substantial improvements in CMC detection.
功能性皮质-肌肉耦合通常通过皮质-肌肉相干性（CMC）分析来评估，这是一种衡量脑电图（EEG）和肌电图（EMG）信号之间线性依赖性的指标。然而，EEG和EMG信号中的噪声可能会超过同步成分的强度，给可靠检测CMC带来挑战。本研究引入了一种基于加权变量误差（EIV）建模的方法，从有噪声的EEG和EMG信号中提取控制运动的皮质和肌肉信号的相关版本，旨在增强相干性估计。提出了两种用于识别潜在EIV系统的算法：一种采用总体最小二乘法，另一种采用加权总体最小二乘法，其中将观测值的不等方差知识纳入回归。使用合成数据和神经生理学数据评估了所提出方法的有效性，结果显示在CMC检测方面有显著改进。
